2024's QS Ranking Visual Landscape for UK Universities¶

Introduction

In the ever-changing higher education landscape, the QS World University Rankings have emerged as a light of excellence, guiding students, scholars, and institutions to the pinnacles of intellectual success (Sowter, Reggio and Hijazi, 2017). The QS Ranking 2024 reflects the tireless pursuit of knowledge and unreserved dedication to academic excellence. This comprehensive rating methodology assesses colleges on various criteria, including academic reputation, employer reputation, professor-student ratios, international faculty and student representation, research network collaboration, and employment results. Further, this blog will visually help to understand more about 2024’s QS ranking for UK universities (Polyakov et al., 2022).

Data Collection and Preparation

Data for this visual journey was carefully collected from the renowned QS Ranking website, a reliable source for global university rankings. However, the raw data needed to be thoroughly cleaned and prepared before it could be shown. Missing values and inconsistencies were handled using a thorough data cleaning process, and the data was turned into a cohesive and organized format, ready for transformation into intelligent and appealing graphic representations.

About the Dataset

The Dataset considered is a Dataset.csv file with data for 1497 Institutions worldwide. With data fields describing QS Ranks in the years 2023 and 2024, Institution Name, Institution City, Location (Country in which the institution is located), SIZE (Describes the sizes as Medium, Large or Extra Large institutions as per various paraments by QS Rankings), STATUS (Which Describes the categories as A or B). It also includes the below factors in terms of SCORE and RANK.

  • Academic Reputation
  • Faculty Student
  • International Faculty
  • International Students
  • International Research Network
  • Employment Outcomes

And an Overall SCORE which describes a final score from QS rankings to the institutions.

In [3]:
#Importing all the required libraries to run the below code
import pandas as pd
# Creating a dataframe by reading the CSV file onto it using pandas
df = pd.read_csv('Dataset.csv',encoding='latin-1')
# Filter the DataFrame to include only rows where the 'Country' column is 'United Kingdom'
uk_universities = df[df['Location'] == 'United Kingdom']
# Printing the dataframe created, data printed is pre-processed
uk_universities.head()

#In the above code df has the worldwide universities and uk_universities has a list of 90 universities in the UK
Out[3]:
2024 RANK 2023 RANK Institution Name Institution City Location SIZE STATUS Academic Reputation SCORE Academic Reputation RANK Employer Reputation SCORE ... Faculty Student RANK International Faculty SCORE International Faculty RANK International Students SCORE International Students RANK International Research Network SCORE International Research Network RANK Employment Outcomes SCORE Employment Outcomes RANK Overall SCORE
0 2 2 University of Cambridge Cambridge United Kingdom L A 100.0 3 100.0 ... 14 100.0 64 95.8 85 99.9 7 100.0 6 99.2
1 3 4 University of Oxford Oxford United Kingdom L A 100.0 2 100.0 ... 8 98.2 110 98.2 60 100.0 1 100.0 3 98.9
4 6 6= Imperial College London London United Kingdom L A 98.3 23 99.4 ... 42 100.0 61 100.0 14 96.7 35 83.0 45 97.8
7 9 8 UCL London United Kingdom XL A 99.5 13 97.9 ... 43 99.1 96 100.0 15 100.0 2 51.6 126 92.4
20 22 15 The University of Edinburgh Edinburgh United Kingdom XL A 98.1 25 96.9 ... 176 98.8 100 99.9 33 99.6 12 48.1 141 86.1

5 rows × 22 columns

Discovering the World Map of Excellence through A Choropleth Chart

This portfolio begins with a broad perspective of the global pattern of academic brilliance, as we reveal an amazing choropleth map which is made up of coloured polygons and used to show spatial changes in a quantity (“Choropleth Maps”) depicting the distribution of 1,497 institutions worldwide, with each shade indicating the overall score earned in the QS Ranking 2024. This immersive visualization illustrates the geographical variety of top-performing institutions and serves as a canvas for our investigation of the UK's academic ecosystem.

In [4]:
#Importing Plotly libraries to run the below code to plot the map
import plotly.graph_objects as go
import pandas as pd
import plotly.offline as pyo
# Code Create the choropleth map referred from the week 8 code bits
fig = go.Figure(data=go.Choropleth(
    locations=df['Location'],
    z=df['2024 RANK'],
    locationmode='country names',
    colorscale='Viridis',
    colorbar_title='2024 RANK'
))
fig.update_layout(
    title='2024 RANK Distribution of universities by Country',
    geo=dict(
        showframe=False,
        projection_type='equirectangular'
    )
)
#Printing the map
fig.show()

#The above code is written about https://plotly.com/python/choropleth-maps/

Navigating the UK's Academic Landscape with A Map of Green Pointers

As the focus turns towards the United Kingdom, a series of green points appear on the map, each indicating an institution within its borders. These points are strategically positioned using accurate latitude and longitude coordinates computed by Python's geocoding library called Geopy (“Geopy: Python Geocoding Toolbox”). This makes it simple for Python developers to get the coordinates of addresses, cities, nations, and landmarks throughout the world using third-party geocoders and other data sources. This layer of geographical specificity allows us to determine the spatial distribution of academic quality, laying the groundwork for our in-depth investigation of the elements that influence the QS Ranking.

In [14]:
#Importing all the required libraries to run the below code
import pandas as pd
import folium
from geopy.geocoders import Nominatim

# Filter for institutions in the UK
uk_institutions = df[df['Location'] == 'United Kingdom']
# Initialize the Nominatim geocoder
geolocator = Nominatim(user_agent="my_application")
# Create a base map
uk_map = folium.Map(location=[54.5, -6], zoom_start=6)
# Add markers for each institution
for index, row in uk_institutions.iterrows():
    # Get the institution's city
    institution_city = row['Institution City']
    # Geocode the institution's city
    location = geolocator.geocode(institution_city)
    # If a location is found, add a marker
    if location:
        folium.Marker(
            location=[location.latitude, location.longitude],
            tooltip=f"{row['Institution Name']}, {institution_city}",
            icon=folium.Icon(color='green')
        ).add_to(uk_map)
# Display the map
uk_map

#The above code is written in reference to https://geopy.readthedocs.io/en/stable/
Out[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Understanding the Factors through A Radar Chart for Institutional Evaluation

After figuring out the UK Universities, To better understand the varied structure of the QS position methodology, further portfolio presents a compelling radar graphic that reveals the deep interaction of elements that influence an institution's position. This visualization is similar to a kaleidoscope of academic excellence and provides a comprehensive view of the criteria used to evaluate and rank universities, such as academic reputation, employer reputation, faculty-student ratios, international faculty and student representation, research network collaboration, and employment outcomes. As per (Hymes) Radar charts are used to demonstrate the data distribution, how much each data point differs from the average, or to find outliers.Furthermore, To plot this NumPy and matplotlib libraries are used.

In [8]:
#Importing all the required libraries to run the below code
import matplotlib.pyplot as plt
import numpy as np

# Selecting the ranking factors
factors = ['Academic Reputation RANK', 'Employer Reputation RANK', 'Faculty Student RANK',
           'International Faculty RANK', 'International Students RANK', 'International Research Network RANK',
           'Employment Outcomes RANK']

# Creates the radar chart
labels = np.linspace(start=0, stop=2*np.pi, num=len(factors), endpoint=False)
values = uk_universities[factors].values[0]  # Assumeing only one row in the data for simplicity

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, polar=True)
ax.set_theta_offset(np.pi / 2)  # To Rotate the chart by 90 degrees
ax.set_rlabel_position(30)  # To Move radial labels away from plotted line
ax.set_xticks(labels)
ax.set_xticklabels(factors)
ax.plot(labels, values, 'bo-', linewidth=2)
ax.fill(labels, values, alpha=0.1)
ax.set_title('Ranking Factors for Institution', pad=30)

plt.show()

#The above code is written in reference to https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_demo.html and Week 4 code reference

Analysing University Sizes with a Histogram of UK Institutions

Exploring further into the UK institutions, we move on to a histogram which gathers numerous data points and arranges them into logical ranges or bins to compress a data series into a graphic that is simple to understand (Chen). Which shows the distribution of institution sizes across the country's academic environment. This graphic depiction enables us to understand the range of educational institutions, ranging from intimate settings that nurture close-knit communities to huge, spreading campuses bursting with intellectual conversation and innovation. This plotting shows that UK Institutions have a max of Large-sized universities and very few small ones.

In [9]:
#Importing all the required libraries to run the below code
import plotly.graph_objects as go
import pandas as pd

# Create the histogram
fig = go.Figure(data=[go.Histogram(x= uk_universities['SIZE'], nbinsx=20, text=uk_universities['SIZE'].value_counts().sort_index())])
# Customize the plot layout
fig.update_layout(
    title='Distribution of Institution Sizes',
    xaxis_title='Overall Score',
    yaxis_title='Count',
    bargap=0.1,  # Adjust the gap between bars
)
# Show the plot
fig.show()

#The above code is written in reference to https://plotly.com/python/histograms/ and Week 3 Code reference

An Excellence Bar Chart for UK Universities

Just as we are about to reach the apex of our visual tour, a massive bar graph appears that displays the UK institutions' rankings. According to (Plotly), In the Bar Graph, Every bar symbolizes an establishment, with its height signifying its total rating and standing in the QS Ranking 2024. The accomplishments of these intellectual strongholds are honoured, and this striking image also acts as a call to action for ongoing development and unshakable commitment to scholarly greatness.This bar graph shows all 90 Universities in The UK that are considered under QS rankings as per their ranks.The bars have the university name and are plotted against the Overall SCORE for the institutions.

In [10]:
#Importing all the required libraries to run the below code
import plotly.graph_objects as go
import pandas as pd
import plotly.offline as pyo

# To Create the bar chart
fig = go.Figure(go.Bar(
    y= uk_universities['Overall SCORE'],
    text= uk_universities['Institution Name']  # Display institution names as text
))
fig.update_layout(
    xaxis_tickangle=-45,  # Rotate x-axis labels
    yaxis_title='Overall Score',
    title='Overall Scores Across Institutions',
    height=600  # Adjust the height of the figure
)
#To display the bar graph
fig.show()

#The above code is written in reference to Week 4 code snips and https://plotly.com/python/bar-charts/

Demonstrating the UK's Academic Excellence Focal points

To further our investigation of the academic environment in the UK, we make use of an eye-catching word cloud. According to (Word Clouds & the Value of Simple Visualizations – Boost Labs) A cluster, or word cloud, is an assortment of words displayed in varying sizes. A word's prominence and frequency of mention within a text are indicated by its size and boldness. The cities that act as hubs for academic excellence are emphasized in this dynamic representation, where the size and prominence of each word correspond to the number of universities in that region. We can pinpoint the areas that have fostered vibrant intellectual communities and supported the expansion and sharing of information, thanks to this striking depiction. As per our word cloud, it clearly shows that London has a maximum number of universities and contributes to Academic excellence.

In [15]:
#Importing all the required libraries to run the below code
import pandas as pd
from wordcloud import WordCloud
# Grouping the universities by city
city_groups = uk_universities.groupby('Institution City')['Institution Name'].apply(list)
# To Create a dictionary with city names and university counts
city_counts = {city: len(universities) for city, universities in city_groups.items()}
# To Generate the word cloud
wordcloud = WordCloud().generate_from_frequencies(city_counts)
# To Display the word cloud
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

#The above code is in reference to https://www.datacamp.com/tutorial/wordcloud-python and Week 5 code reference

Heatmap for Discovering The Correlation within the Ranking Factors

An interesting correlation heatmap reveals the complex web of interactions between the many ranking parameters as we dig further into the workings of the QS Ranking system. Heatmaps are The Data presented graphically, with colours used to signify values. This makes data analysis easier by integrating quantitative and qualitative information (Hotjar). This graphic shows how these components are related to one another and highlights the underlying dynamics that influence an institution's overall performance and ranking. Plotted using Seaborn a Python data visualization library based on matplotlib which provides a high-level interface for drawing attractive and informative statistical graphics (seaborn).The below heatmap shows the relationships between the several rating parameters utilized in the QS rating system are depicted. A significant positive correlation between the factors is indicated by the vivid red hue, whilst a weaker correlation is represented by the blue colour. It sheds light on the relationships between various institutional performance factors and how those relationships affect the overall rating.

In [16]:
#Importing all the required libraries to run the below code
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# To Create the correlation matrix
corr_matrix = uk_universities[['Academic Reputation SCORE', 'Employer Reputation SCORE', 'International Faculty SCORE',
                    'International Students SCORE', 'Employment Outcomes SCORE']].corr()

# To Create the heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap of Ranking Factors')
plt.show()

#The above code is written in Reference to https://www.kaggle.com/code/vanooshenzr/exploring-university-rankings-their-performane

Conclusion

Overall, this portfolio demonstrates various data visualization methods by producing impactful, succinct, and understandable visual data representations. This facilitates data-driven decision-making procedures and proficiently conveys intricate details via aesthetically pleasing and perceptive visualizations.

In summary, It all began with a comprehensive investigation of the QS Ranking 2024 with this visual challenge, revealing the rich fabric of global academic brilliance. Every visualization has provided a different window into the intricate interactions between variables that determine institutional reputation, from the broad strokes of the global choropleth map to the fine details shown by correlation heatmaps and radar charts. The overall portfolio has been enlightening, highlighting the geographic distribution of elite universities, the subtleties of employer and academic reputations, the significance of diversity and global networks, and the ultimate goal of extraordinary employment outcomes. It’s a reminder that, when we consider these visual insights achieving academic brilliance is a constant process that requires creative thinking, and a persistent commitment to expanding knowledge. As a light of excellence, the QS Ranking 2024 directs academic institutions, instructors, and students to the highest levels of scholarly accomplishment.

References

  1. Data Set download from: https://www.topuniversities.com/world-university-rankings
  2. Sowter, B., Reggio, D. and Hijazi, S., 2017. QS World University rankings. In Research analytics (pp. 121-136). Auerbach Publications.
  3. Polyakov, M., Bilozubenko, V., Korneyev, M. and Nebaba, N., 2022. Analysis of key university leadership factors based on their international rankings (QS World University Rankings and Times Higher Education).
  4. “Choropleth Maps.” Plotly.com, plotly.com/python/choropleth-maps/. Accessed 2 May 2024
  5. Code Reference for Chropleth Map: From Week 8 exercises and in reference with https://plotly.com/python/choropleth-maps/
  6. “Geopy: Python Geocoding Toolbox.” PyPI, pypi.org/project/geopy/#:~:text=geopy%20is%20a%20Python%20client. Accessed 3 May 2024.
  7. Code Reference for Geocoders and Map pointers: https://geopy.readthedocs.io/en/stable/
  8. Hymes, Jackie. “Why Should You Use a Radar Chart?” Matchbox Design Group, 23 Nov. 2022, matchboxdesigngroup.com/blog/why-you-should-use-radar-chart/.
  9. Code Reference for Radar Chart: https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_demo.html and Week 4 Code reference.
  10. Chen, James. “Histogram Definition.” Investopedia, 6 July 2022, www.investopedia.com/terms/h/histogram.asp.
  11. Code Reference for Histograms: https://plotly.com/python/histograms/ and Week 3 Code reference
  12. Plotly. “Bar Charts.” Plotly.com, plotly.com/python/bar-charts/.
  13. Code Reference for Bar Chart:Week 4 code snips and https://plotly.com/python/bar-charts/
  14. Word Clouds & the Value of Simple Visualizations – Boost Labs. boostlabs.com/what-are-word-clouds-value-simple-visualizations/.
  15. Code reference for Word Cloud:https://www.datacamp.com/tutorial/wordcloud-python and Week 5 code reference.
  16. Hotjar. “What Are Heat Maps? Guide to Heatmaps/How to Use Them.” Hotjar, www.hotjar.com/heatmaps/.
  17. seaborn. “Seaborn: Statistical Data Visualization — Seaborn 0.9.0 Documentation.” Pydata.org, 2012, seaborn.pydata.org/.
  18. Code Reference for Heatmaps:https://www.kaggle.com/code/vanooshenzr/exploring-university-rankings-their-performane
  19. Seaborn tutorial: https://seaborn.pydata.org/tutorial.html
  20. Python data science and plotting libraries: Usman Ahmed and Suresh Kumar Mukhiya (2020) Hands-On Exploratory Data Analysis with Python. Packt Publishing: https://pdfneed.com/publication/hands-on-exploratory-data-analysis-with-python/